IP Library › Granted Patent US 12,625,285
Granted Patent B2
US 12,625,285 · App. 18/663,471 · Granted May 12, 2026

3D semiconductor detector system

Inventors: Mats Danielsson (Täby, SE); Elias Rieger (Stockholm, SE)
Assignee: SiSnap AB
G01T1/247G01T1/2928
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Quick Facts
Patent No.
US 12,625,285
App. No.
18/663,471
Granted
May 12, 2026
Kind
B2
Abstract

A detector system for molecular imaging of a radionuclide comprises a 3D semiconductor detector comprising a plurality of sensor stacks of sensors made of a semiconductor material having an average atomic number Z below 40. A read-out circuitry connected to the pixels is configured to output, for each interaction induced by an incident gamma ray in the detector, a signal representative of a time, a position and an energy of the interaction in the detector. The interactions in the detector belonging to a same event induced by the incident gamma ray are predicted based on the output signals and used to estimate a direction of the incident gamma ray and reconstruct an image based on the estimated directions of incident gamma rays.

Claims (85)

1 . A detector system for molecular imaging of a radionuclide, comprising:

a three-dimensional (3D) semiconductor detector comprising a plurality of sensor stacks, wherein each sensor stack of the plurality of sensor stacks comprises a plurality of semiconductor sensors each comprising a plurality of pixels, wherein the plurality of semiconductor sensors is made of a semiconductor material having an average atomic number Z below 40;

a read-out circuitry connected to the pixels in the 3D semiconductor detector and configured to output, for each pixel along an electron track in the 3D semiconductor detector, a pixel value representative of an energy deposited at the pixel by a Compton recoil electron along the electron track in the 3D semiconductor detector, wherein the Compton recoil electron is created by a Compton scatter interaction induced by an incident gamma ray in the 3D semiconductor detector;

at least one processor; and

at least one memory comprising instructions, which when executed by the at least one processor, cause the at least one processor to, if an estimated energy of the Compton recoil electron is below a first threshold value, predict a position of a start of the electron track based on a center of a charge cloud in the 3D semiconductor detector by a Gaussian fit to the pixel values output by the read-out circuitry by:

summing pixel values over a first dimension in the 3D semiconductor detector to obtain a first one-dimensional projection,

summing pixel values over a second dimension in the 3D semiconductor detector to obtain a second one-dimensional projection,

fitting a first Gaussian function to the first one-dimensional projection,

fitting a second Gaussian function to the second one-dimensional projection,

determining a first coordinate in the first dimension in the 3D semiconductor detector based on a mean of the first Gaussian function,

determining a second coordinate in the second dimension in the 3D semiconductor detector based on a mean of the second Gaussian function, and

predicting the position of a start of the electron track based on the first coordinate and the second coordinate.

2 . The detector system according to claim 1 , wherein the at least one memory comprising instructions, which when executed by the at least one processor, cause the at least one processor to, if the estimated energy of the Compton recoil electron is below the first threshold value:

estimate a third coordinate in a third dimension in the 3D semiconductor detector based on a width of the first Gaussian function and a width of the second Gaussian function; and

predict the position of the start of the electron track based on the first coordinate, the second coordinate and the third coordinate.

3 . The detector system according to claim 2 , wherein the at least one memory comprising instructions, which when executed by the at least one processor, cause the at least one processor to, if the estimated energy of the Compton recoil electron is below the first threshold value:

compare the value of the third coordinate with a threshold value representing a physical constraint of the plurality of semiconductor sensors in the third dimension in the 3D semiconductor detector; and

determine an updated value of the third coordinate based on the mean of the first Gaussian function and the mean of the second Gaussian function if the value of the third coordinate is below 0 or above the threshold value.

4 . The detector system according to claim 1 , wherein the first threshold value is 100 keV.

5 . The detector system according to claim 1 , wherein the at least one memory comprising instructions, which when executed by the at least one processor, cause the at least one processor to predict the position of the start of the electron track by identifying a pixel position associated with least amount of energy deposition along the electron track in the 3D semiconductor detector if the estimated energy of the Compton recoil electron is above a second threshold value.

6 . The detector system according to claim 5 , wherein the at least one memory comprising instructions, which when executed by the at least one processor, cause the at least one processor to, if the estimated energy of the Compton recoil electron is above the second threshold value:

perform linear regression to fit a line to the pixel values;

compare a pixel value at a start of the line with a pixel value at an end of the line; and

predict the position of the start of the electron track as the one of the start of the line and the end of the line having a pixel value representing a lowest amount of energy deposited at the pixel.

7 . The detector system according to claim 5 , wherein the at least one memory comprising instructions, which when executed by the at least one processor, cause the at least one processor to, if the estimated energy of the Compton recoil electron is above the second threshold value:

sum, for each pixel having a pixel value above a minimum threshold value, pixel values within a pixel area of N×M pixels centered at the pixel; and

predict the position of the start of the electron track based on a position of the pixel having the smallest sum.

8 . The detector system according to claim 7 , wherein the at least one memory comprising instructions, which when executed by the at least one processor, cause the at least one processor to, if the estimated energy of the Compton recoil electron is above the second threshold value:

set each pixel value below the minimum threshold value to zero; and

sum, for each pixel having a non-zero pixel value, pixel values within the pixel area of N×M pixels centered at the pixel.

9 . The detector system according to claim 8 , wherein N=2×k+1, M=2×h+1 and k, h are each a positive integer equal to or larger than one.

10 . The detector system according to claim 9 , wherein k=h.

11 . The detector system according to claim 5 , wherein the second threshold value is 100 keV.

12 . The detector system according to claim 1 , wherein each semiconductor sensor of the plurality of semiconductor sensors comprises:

a plurality of electrodes;

at least one counter electrode; and

an electric field circuitry connected to the plurality of electrodes and the least one counter electrode and configured to apply a bias voltage between each electrode of the plurality of electrodes and a counter electrode of the at least one counter electrode.

13 . The detector system according to claim 1 , wherein the at least one memory comprising instructions, which when executed by the at least one processor, cause the at least one processor to:

estimate a momentum of a Compton recoil electron based on the pixel values output by the read-out circuitry; and

calculate a kinematic constraint for the Compton scatter interaction based on the estimated momentum of the Compton recoil electron.

14 . The detector system according to claim 13 , wherein the at least one memory comprising instructions, which when executed by the at least one processor, cause the at least one processor to estimate the momentum of the Compton recoil electron by a linear fit to a first part of the electron track in the 3D semiconductor detector.

15 . The detector system according to claim 13 , wherein the at least one memory comprising instructions, which when executed by the at least one processor, cause the at least one processor to calculate an opening angle of a constrained cone based on the estimated momentum of the Compton recoil electron; and

the constrained cone restricts a volume in the 3D semiconductor detector, within which a next interaction belonging to the same event induced by the incident gamma ray is allowed to take place.

16 . The detector system according to claim 1 , wherein the at least one memory comprising instructions, which when executed by the at least one processor, cause the at least one processor to estimate a direction of the incident gamma ray by a maximum likelihood estimation based on the pixel values output by the read-out circuitry.

17 . The detector system according to claim 1 , wherein:

the read-out circuitry is configured to output, for each interaction induced by an incident gamma ray in the 3D semiconductor detector, a signal representative of a time, a position and an energy of the interaction in the 3D semiconductor detector; and

the at least one memory comprising instructions, which when executed by the at least one processor, cause the at least one processor to:

predict, based on the pixel output by the read-out circuitry, the interactions in the 3D semiconductor detector belonging to a same event induced by the incident gamma ray;

estimate, based on the predicted interactions in the 3D semiconductor detector belonging to the same event, a direction of the incident gamma ray inducing the same event; and

reconstruct an image based on the estimated directions of incident gamma rays.

18 . The detector system according to claim 1 , wherein the plurality of semiconductor sensors comprises complementary metal oxide semiconductor (CMOS) electronics comprising an application specific integrated circuit (ASIC) comprising analogue to digital converts (ADCs) and the read-out circuitry.

19 . The detector system according to claim 18 , wherein each semiconductor sensor of the plurality of semiconductor sensors is a monolithic semiconductor sensor integrating the CMOS electronics and the plurality of pixels on the monolithic semiconductor sensor.

20 . The detector system according to claim 18 , wherein each semiconductor sensor of the plurality of semiconductor sensors is a hybrid semiconductor sensor comprising the CMOS electronics flip chipped at a side of the plurality of pixels in the semiconductor sensor.

21 . The detector system according to claim 1 , wherein the plurality of semiconductor sensors is made of a semiconductor material having an average atomic number Z below 35.

22 . The detector system according to claim 1 , wherein the semiconductor material is selected from the group consisting of germanium, gallium arsenide, selenium, and silicon.

23 . The detector system according to claim 1 , wherein the 3D semiconductor detector is a 3D silicon detector and each sensor stack of the plurality of sensor stacks comprises a plurality of silicon sensors reach comprising a plurality of pixels.

24 . A detector system for molecular imaging of a radionuclide, comprising:

a three-dimensional (3D) semiconductor detector comprising a plurality of sensor stacks, wherein each sensor stack of the plurality of sensor stacks comprises a plurality of semiconductor sensors each comprising a plurality of pixels, wherein the plurality of semiconductor sensors is made of a semiconductor material having an average atomic number Z below 40;

a read-out circuitry connected to the pixels in the 3D semiconductor detector and configured to output, for each pixel along an electron track in the 3D semiconductor detector, a pixel value representative of an energy deposited at the pixel by a Compton recoil electron along the electron track in the 3D semiconductor detector, wherein the Compton recoil electron is created by a Compton scatter interaction induced by an incident gamma ray in the 3D semiconductor detector;

at least one processor; and

at least one memory comprising instructions, which when executed by the at least one processor, cause the at least one processor to predict a position of a start of the electron track based on a center of a charge cloud in the 3D semiconductor detector if an estimated energy of the Compton recoil electron is below a first threshold value, wherein the first threshold value is 100 keV.

25 . A detector system for molecular imaging of a radionuclide, comprising:

a three-dimensional (3D) semiconductor detector comprising a plurality of sensor stacks, wherein each sensor stack of the plurality of sensor stacks comprises a plurality of semiconductor sensors each comprising a plurality of pixels, wherein the plurality of semiconductor sensors is made of a semiconductor material having an average atomic number Z below 40;

a read-out circuitry connected to the pixels in the 3D semiconductor detector and configured to output, for each pixel along an electron track in the 3D semiconductor detector, a pixel value representative of an energy deposited at the pixel by a Compton recoil electron along the electron track in the 3D semiconductor detector, wherein the Compton recoil electron is created by a Compton scatter interaction induced by an incident gamma ray in the 3D semiconductor detector;

at least one processor; and

at least one memory comprising instructions, which when executed by the at least one processor, cause the at least one processor to, if an estimated energy of the Compton recoil electron is above a threshold value:

perform linear regression to fit a line to the pixel values;

compare a pixel value at a start of the line with a pixel value at an end of the line; and

predict a position of a start of the electron track as the one of the start of the line and the end of the line having a pixel value representing a lowest amount of energy deposited at the pixel.

26 . A detector system for molecular imaging of a radionuclide, comprising:

a three-dimensional (3D) semiconductor detector comprising a plurality of sensor stacks, wherein each sensor stack of the plurality of sensor stacks comprises a plurality of semiconductor sensors each comprising a plurality of pixels, wherein the plurality of semiconductor sensors is made of a semiconductor material having an average atomic number Z below 40;

a read-out circuitry connected to the pixels in the 3D semiconductor detector and configured to output, for each pixel along an electron track in the 3D semiconductor detector, a pixel value representative of an energy deposited at the pixel by a Compton recoil electron along the electron track in the 3D semiconductor detector, wherein the Compton recoil electron is created by a Compton scatter interaction induced by an incident gamma ray in the 3D semiconductor detector;

at least one processor; and

at least one memory comprising instructions, which when executed by the at least one processor, cause the at least one processor to predict a position of a start of the electron track based on a distribution of the energies deposited at each pixel along the electron track in the 3D semiconductor detector, wherein each semiconductor sensor of the plurality of semiconductor sensors comprises:

a plurality of electrodes;

at least one counter electrode; and

an electric field circuitry connected to the plurality of electrodes and the least one counter electrode and configured to apply a bias voltage between each electrode of the plurality of electrodes and a counter electrode of the at least one counter electrode.

27 . A detector system for molecular imaging of a radionuclide, comprising:

a three-dimensional (3D) semiconductor detector comprising a plurality of sensor stacks, wherein each sensor stack of the plurality of sensor stacks comprises a plurality of semiconductor sensors each comprising a plurality of pixels, wherein the plurality of semiconductor sensors is made of a semiconductor material having an average atomic number Z below 40;

a read-out circuitry connected to the pixels in the 3D semiconductor detector and configured to output, for each pixel along an electron track in the 3D semiconductor detector, a pixel value representative of an energy deposited at the pixel by a Compton recoil electron along the electron track in the 3D semiconductor detector, wherein the Compton recoil electron is created by a Compton scatter interaction induced by an incident gamma ray in the 3D semiconductor detector;

at least one processor; and

at least one memory comprising instructions, which when executed by the at least one processor, cause the at least one processor to:

predict a position of a start of the electron track based on a distribution of the energies deposited at each pixel along the electron track in the 3D semiconductor detector;

estimate a momentum of a Compton recoil electron by a linear fit to a first part of the electron track in the 3D semiconductor detector; and

calculate a kinematic constraint for the Compton scatter interaction based on the estimated momentum of the Compton recoil electron.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 22, 2024
From: DANIELSSON, MATS; RIEGER, ELIAS
To: SISNAP AB
Reel/Frame 067488/0796 →
Continuity (1)
Related Publication 20250355125A1 · Nov 20, 2025
References Cited (62)
US 5821541A · Tümer · 1998 [cited by applicant]
US 7800070B2 · Weinberg et al. · 2010 [cited by applicant]
US 8120683B1 · Tumer et al. · 2012 [cited by applicant]
US 10636834B2 · Meylan et al. · 2020 [cited by applicant]
US 11647973B2 · Vija et al. · 2023 [cited by applicant]
US 11817518B2 · Iacobucci et al. · 2023 [cited by applicant]
US 20130026380A1 · Tkaczyk et al. · 2013 [cited by applicant]
US 20140270064A1 · Oh · 2014 [cited by examiner]
US 20140284488A1 · Sanuki · 2014 [cited by examiner]
US 20150331115A1 · Nelson · 2015 [cited by examiner]
US 20160157791A1 · Shizukuishi · 2016 [cited by applicant]
US 20170367665A1 · Schlecht et al. · 2017 [cited by applicant]
US 20180172849A1 · Nelson · 2018 [cited by examiner]
US 20180188392A1 · Polf · 2018 [cited by examiner]
US 20200158663A1 · Danielsson · 2020 [cited by applicant]
US 20200319123A1 · Tanimori · 2020 [cited by examiner]
WO 2008003351A1 · 2008 [cited by applicant]
WO 2022249115A2 · 2022 [cited by applicant]
Chivers et al. “Impact of measuring electron tracks in high-resolution scientific charge-coupled devices within Compton imaging systems”, Nuclear Instruments and Methods in Physics Research A, Elsevier B.V., 2011, p. 24… [cited by examiner]
Kierans et al. “Compton Telescopes for Gamma-ray Astrophysiscs”, astro-ph.IM, 2022, p. 1-76. (Year: 2022). [cited by examiner]
Kierans et al., “Compton Telescopes for Gamma-ray Astrophysics”, arxiv.org, Cornell University Library, Aug. 16, 2022, XP091295582. [cited by applicant]
International Search Report and Written Opinion dated Jul. 18, 2025, issued in corresponding International Patent Application No. PCT/SE2025/050447. [cited by applicant]
Cadoux et al., “The 100uPET project: A small-animal PET scanner for ultra-high resolution molecular imaging with monolithic silicon pixel detectors”, Nuclear Inst. and Methods in Physics Research, A 1048 (2023) 167952. [cited by applicant]
Caputo et al., “The All-sky Medium Energy Gamma-ray Observatory eXplorer (AMEGO-X) Mission Concept”, J. Astron. Telesc Instrum. Syst. 2022 8(4): 044003-1. [cited by applicant]
Cartier et al., “Micrometer-resolution imaging using MÖNCH: towards G2-less grating interferometry”, J. Synchrotron Rad. (2016) 23, 1462-1473. [cited by applicant]
Corradino et al., “Design and Characterization of Backside Termination Structures for Thick Fully-Depleted MAPS”, Sensors (2021) 21, 3809. https//doi.org/10.3390/s21113809. [cited by applicant]
Corradino et al., “Charge Collection Dynamics of the ARCADIA Passive Pixel Arrays: Laser Characterization and TCAD Modeling”, Front. Phys. (2022) 10: 929251. [cited by applicant]
Corradino et al., “Arcadia Maps process qualification through the electrical characterization of passive pixel arrays”, Journal of Instrumentation (2023) 18: C02045. [cited by applicant]
Corradino et al., “Simulation and first characterization of MAPS test structures with gain for timing applications”, Journal of Instrumentation (2024) 19: C02036. [cited by applicant]
Daniel et al., “Application of a deep learning algorithm to Compton imaging of radioactive point sources with a single planar CdTe pixelated detector”, Nuclear Engineering and Technology, 54 (2022) 1747-1753. [cited by applicant]
De Cilladi et al., “Fully Depleted Monolithic Active Microstrip Sensors: TCAD Simulation Study of an Innovative Design Concept”, Sensors (2021) 21: 1990. [cited by applicant]
Matscheko et al., “Compton spectroscopy in the diagnostic X-ray energy range: II. Effects of scattering material and energy resolution”, Phys. Med. Biol. 1989 34(2):199-208. [cited by applicant]
Kozani et al., “Machine learning-based event recognition in SiFi Compton camera imaging for proton therapy monitoring”, Phys. Med. Biol. 2022 67: 155012. [cited by applicant]
Kroeger et al. “Three-Compton Telescope: Theory, Simulations, and Performance”, IEEE Transactions on Nuclear Science, vol. 49, No. 4, pp. 1887-1892, Aug. 2002. [cited by applicant]
Lee et al., “Evaluation of sequence tracking methods for Compton cameras based on CdZnTe arrays”, Nuclear Engineering and Technology 53 (2021) pp. 4080-4092. [cited by applicant]
Lehner et al, “4pi Compton Imaging Using a 3-D Position-Sensitive CdZnTe Detector Via Weighted List-Mode Maximum Likelihood”, IEEE Transactions on Nuclear Science, vol. 51, No. 4, pp. 1618-1624, Aug. 2004. [cited by applicant]
Li et al., “A feasibility study of PETiPIX: an ultra high resolution small animal PET scanner”, 2013 JINST. 8 P12004. [cited by applicant]
Martin et al., “A Ring Compton Scatter Camera for Imaging Medium Energy Gamma Rays”, IEEE Transactions on Nuclear Science, 1993 40(4): 972-978. [cited by applicant]
Maxim et al., “Analytical inversion of the Compton transform using the full set of available projections”, Inverse Problems, 2009 25(9): 095001. [cited by applicant]
Maxim, “Filtered Backprojection Reconstruction and Redundancy in Compton Camera Imaging”, IEEE Transactions on Image Processing, 2014 23(1): 332-341. [cited by applicant]
Mi et al., “A stacked prism lens concept for next-generation hard X-ray telescopes”, Nature Astronomy 2019 3: 867-872. [cited by applicant]
Neubüser et al., Sensor design optimization of innovative low-power, large area FD-MAPS for HEP and applied science, Front. Phys. (2021) 9: 625401. [cited by applicant]
Neubüser et al., “Impact of X-ray induced radiation damage on FD-MAPS of the ARCADIA project”, Journal of Instrumentation (2022) 17: C01035. [cited by applicant]
Neubüser et al., “ARCADIA FD-MAPS: Simulation, characterization and perspectives for high resolution timing applications”, Nuclear Inst. And Methods in Physics Research, A (2023) 1048: 167946. [cited by applicant]
Neubüser et al., “First characterization results of ARCADIA FD-MAPS after X-ray irradiation”, Journal of Instrumentation (2023) 18: C01066. [cited by applicant]
Ordonez et al., “Angular Uncertainties due to Geometry and Spatial Resolution in Compton Cameras”, 1998 IEEE Nuclear Science Symposium Conference Record. 1998 IEEE Nuclear Science Symposium and Medical Imaging Conferenc… [cited by applicant]
Pancheri et al., “Design of CMOS Monolithic Avalanche Detectors for charged-particle timing with sub-nanosecond resolution”, IEEE Eurocon 2023—20th International Conference on Smart Technologies, Jul. 6-8, 2023. [cited by applicant]
Peric et al., “High-Voltage CMOS Active Pixel Sensor”, IEEE Journal of Solid-State Circuits, 2021 56(8): 2488-2502. [cited by applicant]
Sgouros et al., “Radiopharmaceutical therapy in cancer: clinical advances and challenges”, Nature Reviews Drug Discovery, 2020 19: 589-608. [cited by applicant]
Sundberg et al., “1-μm spatial resolution in silicon photon-counting CT detectors”, J. Med. Imag. 2021 8(6): 063501. [cited by applicant]
Sundberg et al., “Compton coincidence in silicon photon-counting CT detectors”, Journal of Medical Imaging, vol. 9 (1), 013501-1-013501-30, Jan./Feb. 2022. [cited by applicant]
Takashima et al, “Event reconstruction of Compton telescopes using a multi-task neural network”, Nuclear Inst. and Methods in Physics Research, A 1038 (2022) 166897. [cited by applicant]
Tashenov et al, “TANGO-New tracking AIGOrithm for gamma-rays”, Nuclear Instruments and Methods in Physics Research A 622 (2010) 592-601. [cited by applicant]
Tian et al, “Radiopharmaceutical imaging based on 3D-CZT Comtpon camera with 3D-printed mouse phantom”, Physica Medica 96 (2022) 140-148. [cited by applicant]
Uenomachi et al., “Simultaneous in vivo imaging with PET and SPECT tracers using a Compton-PET hybrid camera”, Scientific Reports, 11, 17933 (2021). https://doi.org/10.1038/s41598-021-97302-7. [cited by applicant]
Valerio et al., “A monolithic ASIC demonstrator for the Thin Time-of-Flight PET scanner”, 2019 JINST 14 P07013. [cited by applicant]
Wilderman et al., “List-Mode Maximum Likelihood Reconstruction of Compton Scatter Camera Images in Nuclear Medicine”, IEEE Conference Record of Nuclear Science Symposium 1998 3: 1716-1720. [cited by applicant]
Yao et al., “Technical note: Rapid and high-resolution deep learning-based radiopharmaceutical imaging with 3D-CZT Compton camera and sparse projection data”, Med Phys. (2022) 49: 7336-7346. [cited by applicant]
Yoneda et al., “Reconstruction of multiple Compton scattering events in MeV gamma-ray Compton telescopes towards GRAMS: The physics-based probabilistic model”, Astroparticle Physics 144 (2023) 102765. [cited by applicant]
Zambito et al., “20 ps time resolution with a fully-efficient monolithic silicon pixel detector without internal gain layer”, 2023 JINST 18 P03047. [cited by applicant]
OECD, “PENELOPE 2018: A code system for Monte Carlo simulation of electron and photon transport”, Workshop Proceedings, Barcelona, Spain, Jan. 28-Feb. 1, 2019. Paris: Organisation for Economic Co-operation and Developme… [cited by applicant]
Sundberg et al., “Silicon photon-counting detector for full-field CT using an ASIC with adjustable shaping time”, Journal of Medical Imaging, 2020, 7(5): 053503. [cited by applicant]